regressor_parse_example_spec: Generates Parsing Spec for TensorFlow Example to be Used with...

Description Usage Arguments Value Raises See Also

View source: R/parsing_utils.R

Description

If users keep data in tf$Example format, they need to call tf$parse_example with a proper feature spec. There are two main things that this utility helps:

Usage

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regressor_parse_example_spec(
  feature_columns,
  label_key,
  label_dtype = tf$float32,
  label_default = NULL,
  label_dimension = 1L,
  weight_column = NULL
)

Arguments

feature_columns

An iterable containing all feature columns. All items should be instances of classes derived from _FeatureColumn.

label_key

A string identifying the label. It means tf$Example stores labels with this key.

label_dtype

A tf$dtype identifies the type of labels. By default it is tf$float32.

label_default

used as label if label_key does not exist in given tf$Example. By default default_value is none, which means tf$parse_example will error out if there is any missing label.

label_dimension

Number of regression targets per example. This is the size of the last dimension of the labels and logits Tensor objects (typically, these have shape [batch_size, label_dimension]).

weight_column

A string or a _NumericColumn created by column_numeric defining feature column representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. If it is a string, it is used as a key to fetch weight tensor from the features. If it is a _NumericColumn, raw tensor is fetched by key weight_column$key, then weight_column$normalizer_fn is applied on it to get weight tensor.

Value

A dict mapping each feature key to a FixedLenFeature or VarLenFeature value.

Raises

See Also

Other parsing utilities: classifier_parse_example_spec()


tfestimators documentation built on Aug. 10, 2021, 1:06 a.m.